This value must be higher than Correct or remove DAGs that cause problems to. Python version is 2.7.5.INFO - Job 129997: Subtask wait_for_a_minute /srv/airflow_venv2.2.3/lib/python3.7/site-packages/airflow/configuration. Increase parameters related to DAG parsing: dag-file-processor-timeout to at least 180 seconds (or more, if required). I have aready changed /dev/shm directory permissions to 1777 and added this into path bind variable. Sl = self._semlock = _multiprocessing.SemLock(kind, value, maxvalue) Increase parameters related to DAG parsing: dag-file-processor-timeout to at least 180 seconds (or more, if required). If you use the CeleryExecutor, you may want to confirm that this works both where the scheduler runs as well as where the worker runs. The topics on this page contains resolutions to Apache Airflow v1.10.12 Python dependencies, custom plugins, DAGs, Operators, Connections, tasks, and Web server issues you may encounter on an Amazon Managed Workflows for Apache Airflow environment. This port is needed to connect to the Amazon Aurora. We recommend the following steps: Confirm that your VPC security group allows inbound access to port 5432. You can also run airflow tasks list foodagid -tree and confirm that your task shows up in the list as expected. If the scheduler doesnt appear to be running, or the last 'heart beat' was received several hours ago, your DAGs may not appear in Apache Airflow, and new tasks will not be scheduled. We recommend the following steps: Confirm that your VPC security group allows inbound access to port 5432. To test this, you can run airflow dags list and confirm that your DAG shows up in the list. Self._result_queue = multiprocessing.Queue()įile "/usr/lib64/python2.7/multiprocessing/ init.py", line 218, in Queueįile "/usr/lib64/python2.7/multiprocessing/queues.py", line 63, in initįile "/usr/lib64/python2.7/multiprocessing/synchronize.py", line 147, in initįile "/usr/lib64/python2.7/multiprocessing/synchronize.py", line 75, in init If the scheduler doesn't appear to be running, or the last 'heart beat' was received several hours ago, your DAGs may not appear in Apache Airflow, and new tasks will not be scheduled. Processor = self._processor_factory(file_path, log_file_path)įile "/usr/lib/python2.7/site-packages/airflow/jobs.py", line 1301, in processor_factoryįile "/usr/lib/python2.7/site-packages/airflow/jobs.py", line 257, in init Simple_dags = processor_manager.heartbeat()įile "/usr/lib/python2.7/site-packages/airflow/utils/dag_processing.py", line 622, in heartbeat If youre using greater than 50 of your environments capacity you may start overwhelming the Apache Airflow Scheduler. Dissecting the Airflow scheduler Configuring Airflow to scale horizontally. When i start it, it runs fine and DAGs do run, but after few hours it goes down with following error:įile "/usr/lib/python2.7/site-packages/airflow/bin/cli.py", line 882, in schedulerįile "/usr/lib/python2.7/site-packages/airflow/jobs.py", line 200, in runįile "/usr/lib/python2.7/site-packages/airflow/jobs.py", line 1312, in _executeįile "/usr/lib/python2.7/site-packages/airflow/jobs.py", line 1409, in _execute_helper Airflow parses DAGs whether they are enabled or not. Sequential Executor runs one thing at a time so it cannot run heartbeat and task at the same time. If the heartbeat detects that a task was marked as success, it cannot distinguish whether the task itself succeeded or that Airflow was told to consider the task successful. the job has not heartbeat in this many seconds, the scheduler will mark the. Solution 1 I think it is expected for Sequential Executor. Race condition between the heartbeat callback and exit callbacks in the localtaskjob, which monitors the execution of the task. I am running 3-4 python DAGs.The issue is that the scheduler goes down after 4-5 hours. If set to 0 - no retries will be performed.
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